LetsGrowMore¶

Name : Varsha Reddy¶

Task - Exploratory Data Analysis on Dataset -Terrorism¶

Dataset: https://bit.ly/2TK5Xn5¶

Level : Intermediate Level¶

Batch : August 2022¶

Importing the libraries for Data Manipulation and Data Visualization¶

In [1]:
import pandas as pd
import numpy as np
In [2]:
import seaborn as sns
import matplotlib.pyplot as plt
In [3]:
import plotly.offline as py
import plotly.graph_objs as go
import plotly.express as px
from collections import Counter
import math
import warnings
warnings.filterwarnings('ignore')

Importing the Dataset¶

In [4]:
df = pd.read_csv("globalterrorismdb_0718dist.csv",encoding = "ISO-8859-1")

Preprocessing the Data¶

In [5]:
df.head()
Out[5]:
eventid iyear imonth iday approxdate extended resolution country country_txt region ... addnotes scite1 scite2 scite3 dbsource INT_LOG INT_IDEO INT_MISC INT_ANY related
0 1.970000e+11 1970 7 2 NaN 0 NaN 58 Dominican Republic 2 ... NaN NaN NaN NaN PGIS 0 0 0 0 NaN
1 1.970000e+11 1970 0 0 NaN 0 NaN 130 Mexico 1 ... NaN NaN NaN NaN PGIS 0 1 1 1 NaN
2 1.970010e+11 1970 1 0 NaN 0 NaN 160 Philippines 5 ... NaN NaN NaN NaN PGIS -9 -9 1 1 NaN
3 1.970010e+11 1970 1 0 NaN 0 NaN 78 Greece 8 ... NaN NaN NaN NaN PGIS -9 -9 1 1 NaN
4 1.970010e+11 1970 1 0 NaN 0 NaN 101 Japan 4 ... NaN NaN NaN NaN PGIS -9 -9 1 1 NaN

5 rows × 135 columns

In [6]:
df.columns
Out[6]:
Index(['eventid', 'iyear', 'imonth', 'iday', 'approxdate', 'extended',
       'resolution', 'country', 'country_txt', 'region',
       ...
       'addnotes', 'scite1', 'scite2', 'scite3', 'dbsource', 'INT_LOG',
       'INT_IDEO', 'INT_MISC', 'INT_ANY', 'related'],
      dtype='object', length=135)
In [7]:
df.rename(columns={'iyear':'Year','imonth':'Month','iday':'Day','city':'City','country_txt':'Country','provstate':'State','region_txt':'Region','attacktype1_txt':'AttackType','target1':'Target','nkill':'Killed','nwound':'Wounded','summary':'Summary','gname':'Group','targtype1_txt':'Target_type','weaptype1_txt':'Weapon_type','motive':'Motive'},inplace=True)
df=df[['Year','Month','Day','City','Country','State','Region','AttackType','Target','Killed','Wounded','Summary','Group','Target_type','Weapon_type','Motive']]
df.head()
Out[7]:
Year Month Day City Country State Region AttackType Target Killed Wounded Summary Group Target_type Weapon_type Motive
0 1970 7 2 Santo Domingo Dominican Republic NaN Central America & Caribbean Assassination Julio Guzman 1.0 0.0 NaN MANO-D Private Citizens & Property Unknown NaN
1 1970 0 0 Mexico city Mexico Federal North America Hostage Taking (Kidnapping) Nadine Chaval, daughter 0.0 0.0 NaN 23rd of September Communist League Government (Diplomatic) Unknown NaN
2 1970 1 0 Unknown Philippines Tarlac Southeast Asia Assassination Employee 1.0 0.0 NaN Unknown Journalists & Media Unknown NaN
3 1970 1 0 Athens Greece Attica Western Europe Bombing/Explosion U.S. Embassy NaN NaN NaN Unknown Government (Diplomatic) Explosives NaN
4 1970 1 0 Fukouka Japan Fukouka East Asia Facility/Infrastructure Attack U.S. Consulate NaN NaN NaN Unknown Government (Diplomatic) Incendiary NaN
In [8]:
df.isnull().sum()
Out[8]:
Year                0
Month               0
Day                 0
City              434
Country             0
State             421
Region              0
AttackType          0
Target            636
Killed          10313
Wounded         16311
Summary         66129
Group               0
Target_type         0
Weapon_type         0
Motive         131130
dtype: int64
In [9]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 181691 entries, 0 to 181690
Data columns (total 16 columns):
 #   Column       Non-Null Count   Dtype  
---  ------       --------------   -----  
 0   Year         181691 non-null  int64  
 1   Month        181691 non-null  int64  
 2   Day          181691 non-null  int64  
 3   City         181257 non-null  object 
 4   Country      181691 non-null  object 
 5   State        181270 non-null  object 
 6   Region       181691 non-null  object 
 7   AttackType   181691 non-null  object 
 8   Target       181055 non-null  object 
 9   Killed       171378 non-null  float64
 10  Wounded      165380 non-null  float64
 11  Summary      115562 non-null  object 
 12  Group        181691 non-null  object 
 13  Target_type  181691 non-null  object 
 14  Weapon_type  181691 non-null  object 
 15  Motive       50561 non-null   object 
dtypes: float64(2), int64(3), object(11)
memory usage: 22.2+ MB
In [10]:
print(df.duplicated().sum())

9579
In [11]:
df.Country.value_counts()[:10]
Out[11]:
Iraq              24636
Pakistan          14368
Afghanistan       12731
India             11960
Colombia           8306
Philippines        6908
Peru               6096
El Salvador        5320
United Kingdom     5235
Turkey             4292
Name: Country, dtype: int64
In [12]:
df.Target.value_counts()
Out[12]:
Civilians                  6461
Unknown                    5918
Soldiers                   3157
Patrol                     2942
Checkpoint                 2905
                           ... 
Manager Sanat Raj             1
Military/ Police patrol       1
Thor 67 Outpost               1
Employee's Vehicle            1
Hmeymim Air Base              1
Name: Target, Length: 86006, dtype: int64
In [13]:
df.Target.value_counts(normalize=True)
Out[13]:
Civilians                  0.035685
Unknown                    0.032686
Soldiers                   0.017437
Patrol                     0.016249
Checkpoint                 0.016045
                             ...   
Manager Sanat Raj          0.000006
Military/ Police patrol    0.000006
Thor 67 Outpost            0.000006
Employee's Vehicle         0.000006
Hmeymim Air Base           0.000006
Name: Target, Length: 86006, dtype: float64
In [14]:
df['Casualities'] = df.Killed +df.Wounded
In [15]:
df.describe(include=["object", "bool"])
Out[15]:
City Country State Region AttackType Target Summary Group Target_type Weapon_type Motive
count 181257 181691 181270 181691 181691 181055 115562 181691 181691 181691 50561
unique 36674 205 2855 12 9 86006 112492 3537 22 12 14490
top Unknown Iraq Baghdad Middle East & North Africa Bombing/Explosion Civilians 09/00/2016: Sometime between September 18, 201... Unknown Private Citizens & Property Explosives Unknown
freq 9775 24636 7645 50474 88255 6461 100 82782 43511 92426 14889
In [16]:
df.sort_values(by="City", ascending=False).head()
Out[16]:
Year Month Day City Country State Region AttackType Target Killed Wounded Summary Group Target_type Weapon_type Motive Casualities
44393 1990 10 24 zinarag Philippines Cagayan Southeast Asia Armed Assault truck 0.0 0.0 NaN New People's Army (NPA) Private Citizens & Property Incendiary NaN 0.0
42281 1990 4 25 yokohama Japan Kanagawa East Asia Facility/Infrastructure Attack residence, japan airport bldg. company * 0.0 0.0 NaN Chukakuha (Middle Core Faction) Business Incendiary NaN 0.0
19135 1983 6 23 yacan Peru Pasco South America Assassination pablo cardenas, justic of peace 2.0 0.0 NaN Shining Path (SL) Government (General) Firearms NaN 2.0
41289 1990 1 24 wakunai Papua New Guinea Bougainville Australasia & Oceania Armed Assault brihan norman islander aircraft 0.0 0.0 NaN Bougainville Revolutionary Army (BRA) Airports & Aircraft Incendiary NaN 0.0
19055 1983 6 13 vinchos Peru Ayacucho South America Assassination constantino supdo, justice of peace 1.0 0.0 NaN Shining Path (SL) Government (General) Firearms NaN 1.0
In [17]:
df[df["AttackType"] == 1].mean()
Out[17]:
Year           NaN
Month          NaN
Day            NaN
City           NaN
Country        NaN
State          NaN
Region         NaN
AttackType     NaN
Target         NaN
Killed         NaN
Wounded        NaN
Summary        NaN
Group          NaN
Target_type    NaN
Weapon_type    NaN
Motive         NaN
Casualities    NaN
dtype: object
In [18]:
df[df["Target_type"]==1].mean()
Out[18]:
Year           NaN
Month          NaN
Day            NaN
City           NaN
Country        NaN
State          NaN
Region         NaN
AttackType     NaN
Target         NaN
Killed         NaN
Wounded        NaN
Summary        NaN
Group          NaN
Target_type    NaN
Weapon_type    NaN
Motive         NaN
Casualities    NaN
dtype: object
In [19]:
df.fillna(0).head()
Out[19]:
Year Month Day City Country State Region AttackType Target Killed Wounded Summary Group Target_type Weapon_type Motive Casualities
0 1970 7 2 Santo Domingo Dominican Republic 0 Central America & Caribbean Assassination Julio Guzman 1.0 0.0 0 MANO-D Private Citizens & Property Unknown 0 1.0
1 1970 0 0 Mexico city Mexico Federal North America Hostage Taking (Kidnapping) Nadine Chaval, daughter 0.0 0.0 0 23rd of September Communist League Government (Diplomatic) Unknown 0 0.0
2 1970 1 0 Unknown Philippines Tarlac Southeast Asia Assassination Employee 1.0 0.0 0 Unknown Journalists & Media Unknown 0 1.0
3 1970 1 0 Athens Greece Attica Western Europe Bombing/Explosion U.S. Embassy 0.0 0.0 0 Unknown Government (Diplomatic) Explosives 0 0.0
4 1970 1 0 Fukouka Japan Fukouka East Asia Facility/Infrastructure Attack U.S. Consulate 0.0 0.0 0 Unknown Government (Diplomatic) Incendiary 0 0.0
In [20]:
df.dropna(axis=0,inplace=True)

Ruinous features of Dataset¶

In [21]:
print("Year with the most attacks:",df['Year'].value_counts().idxmax())

Year with the most attacks: 2011
In [22]:
print("Month with the most attacks:",df['Month'].value_counts().idxmax())

Month with the most attacks: 7
In [23]:
print("Group with the most attacks:",df['Group'].value_counts().index[1])

Group with the most attacks: Taliban
In [24]:
print("Region with the most attacks:",df['Region'].value_counts().idxmax())

Region with the most attacks: South Asia
In [25]:
print("City with the most attacks:",df['City'].value_counts().index[1])

City with the most attacks: Unknown
In [26]:
print("Country with the most attacks:",df['Country'].value_counts().idxmax())

Country with the most attacks: Iraq
In [27]:
print("Most Attack Types:",df['AttackType'].value_counts().idxmax())

Most Attack Types: Bombing/Explosion

Terrorist Attacks by Country in each year through bar plot¶

In [28]:
country = df.groupby('Country').Casualities.sum().to_frame().reset_index()
country.columns =['Country','Casualities']
px.bar(data_frame=country,x='Country',y='Casualities',color='Casualities')

Terrorist Attacks by Month in each year through bar plot¶

In [29]:
month = df.groupby('Month').Casualities.sum().to_frame().reset_index()
month.columns =['Month','Casualities']
px.bar(data_frame=month,x='Month',y='Casualities',color='Casualities')

Terrorist Attacks by Region in each year through bar plot¶

In [30]:
region_attack = df.groupby('Region').Casualities.sum().to_frame().reset_index()
region_attack.columns =['Region','Casualities']
px.bar(data_frame=region_attack,x='Region',y='Casualities',color='Casualities')

plotly figurewidget¶

Conclusion¶

In [31]:
f = go.FigureWidget()
f

FigureWidget({
    'data': [], 'layout': {'template': '...'}
})
In [32]:
f.add_scatter(y=['Year','Month','Day','Killed','Wounded']);
f.add_bar(y=['Region','State','City','Country','Target'])
f.layout.title = 'Terrorist Attacks '
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]: